A focus on structural brain imaging in the Alzheimer's disease neuroimaging initiative

Association of Motor Function With Cognitive Trajectories and Structural Brain Differences: A Community-Based Cohort Study

BACKGROUND AND OBJECTIVES

The association of motor function with cognitive health remains controversial, and the mechanisms underlying this relationship are unclear. We aimed to examine the association between motor function and long-term cognitive trajectories and further explore the underlying mechanisms using brain MRI.

METHODS

In the Rush Memory and Aging Project, a prospective cohort study, a total of 2,192 volunteers were recruited from the communities in northeastern Illinois and followed up for up to 22 years (from 1997 to 2020). Individuals with dementia, disability, missing data on motor function at baseline, and missing follow-up data on cognitive function were excluded. At baseline, global motor function was evaluated using the averaged z scores of 10 motor tests covering dexterity, gait, and hand strength; the composite score was tertiled as low, moderate, or high. Global and domain-specific cognitive functions-including episodic memory, semantic memory, working memory, visuospatial ability, and perceptual speed-were measured annually through 19 cognitive tests. A subsample (n = 401) underwent brain MRI scans and regional brain volumes were measured. Data were analyzed using linear mixed-effects models and linear regression.

RESULTS

Among the 1,618 participants (mean age 79.45 ± 7.32 years) included in this study, baseline global motor function score ranged from 0.36 to 1.82 (mean 1.03 ± 0.22). Over the follow-up (median 6.03 years, interquartile range 3.00-10.01 years), low global motor function and its subcomponents were related to significantly faster declines in global cognitive function (β = -0.005, 95% CI -0.006 to -0.005) and each of the 5 cognitive domains. Of the 344 participants with available MRI data, low motor function was also associated with smaller total brain (β = -25.848, 95% CI -44.902 to -6.795), total white matter (β = -18.252, 95% CI -33.277 to -3.226), and cortical white matter (β = -17.503, 95% CI -32.215 to -2.792) volumes, but a larger volume of white matter hyperintensities (β = 0.257, 95% CI 0.118-0.397).

DISCUSSION

Low motor function is associated with an accelerated decline in global and domain-specific cognitive functions. Both neurodegenerative and cerebrovascular pathologies might contribute to this association.

Using artificial intelligence to learn optimal regimen plan for Alzheimer's disease

BACKGROUND

Alzheimer's disease (AD) is a progressive neurological disorder with no specific curative medications. Sophisticated clinical skills are crucial to optimize treatment regimens given the multiple coexisting comorbidities in the patient population.

OBJECTIVE

Here, we propose a study to leverage reinforcement learning (RL) to learn the clinicians' decisions for AD patients based on the longitude data from electronic health records.

METHODS

In this study, we selected 1736 patients from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. We focused on the two most frequent concomitant diseases-depression, and hypertension, thus creating 5 data cohorts (ie, Whole Data, AD, AD-Hypertension, AD-Depression, and AD-Depression-Hypertension). We modeled the treatment learning into an RL problem by defining states, actions, and rewards. We built a regression model and decision tree to generate multiple states, used six combinations of medications (ie, cholinesterase inhibitors, memantine, memantine-cholinesterase inhibitors, hypertension drugs, supplements, or no drugs) as actions, and Mini-Mental State Exam (MMSE) scores as rewards.

RESULTS

Given the proper dataset, the RL model can generate an optimal policy (regimen plan) that outperforms the clinician's treatment regimen. Optimal policies (ie, policy iteration and Q-learning) had lower rewards than the clinician's policy (mean -3.03 and -2.93 vs. -2.93, respectively) for smaller datasets but had higher rewards for larger datasets (mean -4.68 and -2.82 vs. -4.57, respectively).

CONCLUSIONS

Our results highlight the potential of using RL to generate the optimal treatment based on the patients' longitude records. Our work can lead the path towards developing RL-based decision support systems that could help manage AD with comorbidities.

Using Artificial Intelligence to Learn Optimal Regimen Plan for Alzheimer's Disease

Background

Alzheimer's Disease (AD) is a progressive neurological disorder with no specific curative medications. While only a few medications are approved by FDA (i.e., donepezil, galantamine, rivastigmine, and memantine) to relieve symptoms (e.g., cognitive decline), sophisticated clinical skills are crucial to optimize the appropriate regimens given the multiple coexisting comorbidities in this patient population.

Objective

Here, we propose a study to leverage reinforcement learning (RL) to learn the clinicians' decisions for AD patients based on the longitude records from Electronic Health Records (EHR).

Methods

In this study, we withdraw 1,736 patients fulfilling our criteria, from the Alzheimer's Disease Neuroimaging Initiative(ADNI) database. We focused on the two most frequent concomitant diseases, depression, and hypertension, thus resulting in five main cohorts, 1) whole data, 2) AD-only, 3) AD-hypertension, 4) AD-depression, and 5) AD-hypertension-depression. We modeled the treatment learning into an RL problem by defining the three factors (i.e., states, action, and reward) in RL in multiple strategies, where a regression model and a decision tree are developed to generate states, six main medications extracted (i.e., no drugs, cholinesterase inhibitors, memantine, hypertension drugs, a combination of cholinesterase inhibitors and memantine, and supplements or other drugs) are for action, and Mini-Mental State Exam (MMSE) scores are for reward.

Results

Given the proper dataset, the RL model can generate an optimal policy (regimen plan) that outperforms the clinician's treatment regimen. With the smallest data samples, the optimal-policy (i.e., policy iteration and Q-learning) gained a lesser reward than the clinician's policy (mean -2.68 and -2.76 vs . -2.66, respectively), but it gained more reward once the data size increased (mean -3.56 and -2.48 vs . -3.57, respectively).

Conclusions

Our results highlight the potential of using RL to generate the optimal treatment based on the patients' longitude records. Our work can lead the path toward the development of RL-based decision support systems which could facilitate the daily practice to manage Alzheimer's disease with comorbidities.

Pulmonary function is associated with cognitive decline and structural brain differences

The association of poor pulmonary function (PF) with cognitive trajectories and structural brain differences remains unclear. Within the Rush Memory and Aging Project, 1377 dementia-free subjects were followed up to 21 years. PF was assessed with a composite score measured at baseline. Global and domain-specific cognitive function was assessed annually constructed from 19 cognitive tests. A subsample of 351 participants underwent brain magnetic resonance imaging to investigate the cross-sectional association between PF and structural brain volumes. We found that low PF was related to faster decline in global cognition, and domain-specific function including episodic memory, semantic memory, working memory, visuospatial ability, and perceptual speed. In addition, low PF was associated with smaller volumes of total brain, white matter and gray matter, and larger white matter hyperintensities volume. Our results suggest that low PF is associated with faster cognitive decline, and both neurodegeneration and vascular brain lesions may underlie the association.

Testing a convolutional neural network-based hippocampal segmentation method in a stroke population

As stroke mortality rates decrease, there has been a surge of effort to study poststroke dementia (PSD) to improve long-term quality of life for stroke survivors. Hippocampal volume may be an important neuroimaging biomarker in poststroke dementia, as it has been associated with many other forms of dementia. However, studying hippocampal volume using MRI requires hippocampal segmentation. Advances in automated segmentation methods have allowed for studying the hippocampus on a large scale, which is important for robust results in the heterogeneous stroke population. However, most of these automated methods use a single atlas-based approach and may fail in the presence of severe structural abnormalities common in stroke. Hippodeep, a new convolutional neural network-based hippocampal segmentation method, does not rely solely on a single atlas-based approach and thus may be better suited for stroke populations. Here, we compared quality control and the accuracy of segmentations generated by Hippodeep and two well-accepted hippocampal segmentation methods on stroke MRIs (FreeSurfer 6.0 whole hippocampus and FreeSurfer 6.0 sum of hippocampal subfields). Quality control was performed using a stringent protocol for visual inspection of the segmentations, and accuracy was measured as volumetric correlation with manual segmentations. Hippodeep performed significantly better than both FreeSurfer methods in terms of quality control. All three automated segmentation methods had good correlation with manual segmentations and no one method was significantly more correlated than the others. Overall, this study suggests that both Hippodeep and FreeSurfer may be useful for hippocampal segmentation in stroke rehabilitation research, but Hippodeep may be more robust to stroke lesion anatomy.

A deep learning toolbox for automatic segmentation of subcortical limbic structures from MRI images

A tool was developed to automatically segment several subcortical limbic structures (nucleus accumbens, basal forebrain, septal nuclei, hypothalamus without mammillary bodies, the mammillary bodies, and fornix) using only a T1-weighted MRI as input. This tool fills an unmet need as there are few, if any, publicly available tools to segment these clinically relevant structures. A U-Net with spatial, intensity, contrast, and noise augmentation was trained using 39 manually labeled MRI data sets. In general, the Dice scores, true positive rates, false discovery rates, and manual-automatic volume correlation were very good relative to comparable tools for other structures. A diverse data set of 698 subjects were segmented using the tool; evaluation of the resulting labelings showed that the tool failed in less than 1% of cases. Test-retest reliability of the tool was excellent. The automatically segmented volume of all structures except mammillary bodies showed effectiveness at detecting either clinical AD effects, age effects, or both. This tool will be publicly released with FreeSurfer (surfer.nmr.mgh.harvard.edu/fswiki/ScLimbic). Together with the other cortical and subcortical limbic segmentations, this tool will allow FreeSurfer to provide a comprehensive view of the limbic system in an automated way.

Self-reported sleep relates to hippocampal atrophy across the adult lifespan: results from the Lifebrain consortium

Objectives

Poor sleep is associated with multiple age-related neurodegenerative and neuropsychiatric conditions. The hippocampus plays a special role in sleep and sleep-dependent cognition, and accelerated hippocampal atrophy is typically seen with higher age. Hence, it is critical to establish how the relationship between sleep and hippocampal volume loss unfolds across the adult lifespan.

Methods

Self-reported sleep measures and MRI-derived hippocampal volumes were obtained from 3105 cognitively normal participants (18–90 years) from major European brain studies in the Lifebrain consortium. Hippocampal volume change was estimated from 5116 MRIs from 1299 participants for whom longitudinal MRIs were available, followed up to 11 years with a mean interval of 3.3 years. Cross-sectional analyses were repeated in a sample of 21,390 participants from the UK Biobank.

Results

No cross-sectional sleep—hippocampal volume relationships were found. However, worse sleep quality, efficiency, problems, and daytime tiredness were related to greater hippocampal volume loss over time, with high scorers showing 0.22% greater annual loss than low scorers. The relationship between sleep and hippocampal atrophy did not vary across age. Simulations showed that the observed longitudinal effects were too small to be detected as age-interactions in the cross-sectional analyses.

Conclusions

Worse self-reported sleep is associated with higher rates of hippocampal volume decline across the adult lifespan. This suggests that sleep is relevant to understand individual differences in hippocampal atrophy, but limited effect sizes call for cautious interpretation.

Late-life cynical hostility is associated with white matter alterations and the risk of Alzheimer's disease

BACKGROUND

Cynical hostility (CH), a specific dimension of hostility that consists of a mistrust of others, has been suggested as a high-risk trait for dementia. However, the influence of CH on the incidence of Alzheimer's disease (AD) remains poorly understood. This study investigated whether late-life CH is associated with AD risk and structural neuroimaging markers of AD.

METHODS

In community-dwelling older adults from the French ESPRIT cohort (n = 1388), incident dementia rate according to CH level was monitored during an 8-year follow-up and analyzed using Cox proportional hazards regression models. Brain magnetic resonance imaging volumes were measured at baseline (n = 508). Using automated segmentation procedures (Freesurfer 6.0), the authors assessed brain grey and white volumes on all magnetic resonance imaging scans. They also measured white matter hyperintensities volumes using semi-automated procedures. Mean volumes according to the level of CH were compared using ANOVA.

RESULTS

Eighty-four participants developed dementia (32 with AD). After controlling for potential confounders, high CH was predictive of AD (HR 2.74; 95% CI 1.10-6.85; p = 0.030) and all dementia types are taken together (HR 2.30; 95% CI 1.10-4.80; p = 0.027). High CH was associated with white matter alterations, particularly smaller anterior corpus callosum volume (p < 0.01) after False Discovery Rate correction, but not with grey matter volumes.

CONCLUSIONS

High CH in late life is associated with cerebral white matter alterations, designated as early markers of dementia, and higher AD risk. Identifying lifestyle and biological determinants related to CH could provide clues on AD physiopathology and avenues for prevention strategies.

Joint cortical surface and structural connectivity analysis of Alzheimer's Disease

Prior neuroimaging studies have demonstrated isolated structural and connectivity changes in the brain due to Alzheimer's Disease (AD). However, how these changes relate to each other is not well understood. We present a preliminary study to begin to fill this gap by leveraging joint independent component analysis (jICA). We explore how jICA performs in an analysis of T1 and diffusion weighted MRI by characterizing the joint changes of complex cortical surface and structural connectivity metrics in AD in subjects from the Baltimore Longitudinal Study of Aging. We calculate 588 region-based cortical metrics and 4,753 fractional anisotropy-based connectivity metrics and project them into a low-dimensional manifold with principal component analysis. We perform jICA on the manifold and subsequently backproject the independent components to the original data space. We demonstrate component stability with 3-fold cross validation and find differential component loadings between 776 cognitively unimpaired control subjects and 23 with AD that generalizes across folds. In addition, we perform the same analysis on the surface and connectivity metrics separately and find that the joint approach identifies both novel and similar components to the separate approaches. To illustrate the joint approach's primary utility, we provide an example hypothesis for how surface and connectivity components may vary together with AD. These preliminary results suggest jointly varying independent cortical surface and structural connectivity components can be consistently extracted from MRI data and provide a data-driven way for generating novel hypotheses about AD that may not be captured by separate analyses.

Do 2-year changes in superior frontal gyrus and global brain atrophy affect cognition?

Introduction

Metabolic alterations to the superior frontal gyrus (SFG) have been linked to cognitive decline. Whether these indicate structural atrophy, which could be screened for at a larger scale using noninvasive structural imaging, is unknown.

Methods

We assessed annual structural magnetic resonance imaging scans and cognitive data from 3 consecutive years from 204 participants from the AD Neuroimaging Initiative database (mean age 72.24 [8.175] years). We evaluated associations between brain structural changes and performance in the Montreal Cognitive Assessment, Everyday Cognition Visuospatial subtest (ECog Visuospatial), and Functional Assessment Questionnaire.

Results

Changes in the surface area of the SFG were associated with changes in the outcome of the ECog Visuospatial test (P < .05), but an inconsistent pattern of association was found between the 2-year global brain atrophy progression and changes in the outcome from the three cognitive tests selected.

Discussion

The extent into which (and if) changes in the SFG influence cognition warrant further evaluation in a larger period in more heterogeneous population.

Longitudinal changes in the brain in mild cognitive impairment: a magnetic resonance imaging study using the visual rating method and tensor-based morphometry

Background Brain atrophy is associated with mild cognitive impairment (MCI), and by using volumetric and visual analyzing methods, it is possible to differentiate between individuals with progressive MCI (MCIp) and stable MCI (MCIs). Automated analysis methods detect degenerative changes in the brain earlier and more reliably than visual methods. Purpose To detect and evaluate structural brain changes between and within the MCIs, MCIp, and control groups during a two-year follow-up period. Material and Methods Brain magnetic resonance imaging (MRI) scans of 11 participants with MCIs, 18 participants with MCIp, and 84 controls were analyzed by the visual rating method (VRM) and tensor-based morphometry (TBM). Results At baseline, both VRM and TBM differentiated the whole MCI group (combined MCIs and MCIp) and the MCIp group from the control group, but they did not differentiate the MCIs group from the control group. At follow-up, both methods differentiated the MCIp group from the control group, but minor differences between the MCIs and control groups were only seen by TBM. Neuropsychological tests did not find differences between the MCIs and control groups at follow-up. Neither method revealed relevant signs of brain atrophy progression within or between MCI subgroups during the follow-up time. Conclusion Both methods are equally good in the evaluation of structural brain changes in MCI if the groups are sufficiently large and the disease progresses to AD. Only TBM disclosed minor atrophic changes in the MCIs group compared to controls at follow-up. The results need to be confirmed with a large patient group and longer follow-up time.

Impact of Demographic and Obstetric Factors on Infant Brain Volumes: A Population Neuroscience Study

Individual differences in neuroanatomy are associated with intellectual ability and psychiatric risk. Factors responsible for this variability remain poorly understood. We tested whether 17 major demographic and obstetric variables were associated with individual differences in brain volumes in 756 neonates assessed with MRI. Gestational age at MRI, sex, gestational age at birth, and birthweight were the most significant predictors, explaining 31% to 59% of variance. Unexpectedly, earlier born babies had larger brains than later born babies after adjusting for other predictors. Our results suggest earlier born children experience accelerated brain growth, either as a consequence of the richer sensory environment they experience outside the womb or in response to other factors associated with delivery. In the full sample, maternal and paternal education, maternal ethnicity, maternal smoking, and maternal psychiatric history showed marginal associations with brain volumes, whereas maternal age, paternal age, paternal ethnicity, paternal psychiatric history, and income did not. Effects of parental education and maternal ethnicity are partially mediated by differences in birthweight. Remaining effects may reflect differences in genetic variation or cultural capital. In particular late initiation of prenatal care could negatively impact brain development. Findings could inform public health policy aimed at optimizing child development.

Radiological-Pathological Correlation in Alzheimer's Disease: Systematic Review of Antemortem Magnetic Resonance Imaging Findings

BACKGROUND

The standard method of ascertaining Alzheimer's disease (AD) remains postmortem assessment of amyloid plaques and neurofibrillary degeneration. Vascular pathology, Lewy bodies, TDP-43, and hippocampal sclerosis are frequent comorbidities. There is therefore a need for biomarkers that can assess these etiologies and provide a diagnosis in vivo.

OBJECTIVE

We conducted a systematic review of published radiological-pathological correlation studies to determine the relationship between antemortem magnetic resonance imaging (MRI) and neuropathological findings in AD.

METHODS

We explored PubMed in June-July 2015 using "Alzheimer's disease" and combinations of radiological and pathological terms. After exclusion following screening and full-text assessment of the 552 extracted manuscripts, three others were added from their reference list. In the end, we report results based on 27 articles.

RESULTS

Independently of normal age-related brain atrophy, AD pathology is associated with whole-brain and hippocampal atrophy and ventricular expansion as observed on T1-weighted images. Moreover, cerebral amyloid angiopathy and cortical microinfarcts are also related to brain volume loss in AD. Hippocampal sclerosis and TDP-43 are associated with hippocampal and medial temporal lobe atrophy, respectively. Brain volume loss correlates more strongly with tangles than with any other pathological finding. White matter hyperintensities observed on proton density, T2-weighted and FLAIR images are strongly related to vascular pathologies, but are also associated with other histological changes such as gliosis or demyelination.

CONCLUSION

Cerebral atrophy and white matter changes in the living brain reflect underlying neuropathology and may be detectable using antemortem MRI. In vivo MRI may therefore be an avenue for AD pathological staging.

Automated Segmentation of Head Computed Tomography Images Using FSL

OBJECTIVE

The aim of this study was to investigate the use of one magnetic resonance image-processing tool, FSL, in its ability to perform automated segmentation of computed tomographic images of the brain.

METHODS

Head computed tomography (CT) images were brain extracted and segmented using the FSL tools BET and FAST, respectively. The products of segmentation were analyzed by histogram. The impact of image intensity inhomogeneity correction was investigated using simulated bias fields, 14 routine head CT scans, and selected illustrative clinical cases.

RESULTS

FSL FAST performs direct segmentation of head CT images, permitting quantitation of gray and white matter densities and volumes, achieving a more complete segmentation than masking methods. "Bias field correction" reduced the covariance of image signal intensities of the total brain and gray matter images (P < 0.01). Correction is larger when the effects of beam hardening and radiation scatter are larger, resulting in improved segmentation.

CONCLUSIONS

FSL FAST enables direct segmentation of head CT images.

Test-retest reproducibility of [11C]-L-deprenyl-D2 binding to MAO-B in the human brain

Background

[¹¹C]-l-deprenyl-D₂ is a positron emission tomography (PET) radioligand for measurement of the monoamine oxidase B (MAO-B) activity in vivo brain. The estimation of the test-retest reproducibility is important for accurate interpretation of PET studies.

Results

We performed two [¹¹C]-l-deprenyl-D₂ scans for six healthy subjects and evaluated the test-retest variability of this radioligand. MAO-B binding was quantified by two tissue compartment model (2TCM) with three rate constants (K₁, k₂, k₃) using metabolite-corrected plasma radioactivity. The λk₃ defined as (K₁/k₂) × k₃ was also calculated. The correlation between MAO-B binding and age, and the effect of partial volume effect correction (PVEc) for the reproducibility were also estimated. %difference of k₃ was 2.6% (medial frontal cortex) to 10.3% (hippocampus), and that of λk₃ was 5.0% (thalamus) to 9.2% (cerebellum). Mean %difference of all regions were 5.3 and 7.0% in k₃ and λk₃, respectively. All regions showed below 10% variabilities except the hippocampus in k₃ (10.3%). Intraclass correlation coefficient (ICC) of k₃ was 0.78 (hippocampus) to 0.98 (medial frontal cortex), and that of λk₃ was 0.78 (hippocampus) to 0.95 (thalamus). Mean ICC were 0.94 and 0.89 in k₃ and λk₃, respectively. The highest positive correlation with age was observed in the hippocampus, as r = 0.75 in k₃ and 0.76 in λk₃. After PVEc, mean %difference were 5.6 and 7.2% in k₃ and λk₃, respectively. Mean ICC were 0.92 and 0.90 for k₃ and λk₃, respectively. These values were almost the same as those before PVEc.

Conclusions

The present results indicate that k₃ and λk₃ of [¹¹C]-l-deprenyl-D₂ are reliable parameters for test-retest reproducibility with healthy subjects both before and after PVEc. The studies with patients of larger sample size are required for further clinical applications.

Structural neuroimaging in preclinical dementia: From microstructural deficits and grey matter atrophy to macroscale connectomic changes

The last decade has witnessed a proliferation of neuroimaging studies characterising brain changes associated with Alzheimer's disease (AD), where both widespread atrophy and 'signature' brain regions have been implicated. In parallel, a prolonged latency period has been established in AD, with abnormal cerebral changes beginning many years before symptom onset. This raises the possibility of early therapeutic intervention, even before symptoms, when treatments could have the greatest effect on disease-course modification. Two important prerequisites of this endeavour are (1) accurate characterisation or risk stratification and (2) monitoring of progression using neuroimaging outcomes as a surrogate biomarker in those without symptoms but who will develop AD, here referred to as preclinical AD. Structural neuroimaging modalities have been used to identify brain changes related to risk factors for AD, such as familial genetic mutations, risk genes (for example apolipoprotein epsilon-4 allele), and/or family history. In this review, we summarise structural imaging findings in preclinical AD. Overall, the literature suggests early vulnerability in characteristic regions, such as the medial temporal lobe structures and the precuneus, as well as white matter tracts in the fornix, cingulum and corpus callosum. We conclude that while structural markers are promising, more research and validation studies are needed before future secondary prevention trials can adopt structural imaging biomarkers as either stratification or surrogate biomarkers.

Translational MRI Volumetry with NeuroQuant: Effects of Version and Normative Data on Relationships with Memory Performance in Healthy Older Adults and Patients with Mild Cognitive Impairment

NeuroQuant (NQ) is a fully-automated program that overcomes several existing limitations in the clinical translation of MRI-derived volumetry. The current study characterized differences between the original (NQ1) and an updated NQ version (NQ2) by 1) replicating previously identified relationships between neuropsychological test performance and medial temporal lobe volumes, 2) evaluating the level of agreement between NQ versions, and 3) determining if the addition of NQ2 age-/sex-based z-scores hold greater clinical utility for prediction of memory impairment than standard percent of intracranial volume (% ICV) values. Sixty-seven healthy older adults and 65 mild cognitive impairment patients underwent structural MRI and completed cognitive testing, including the Immediate and Delayed Memory indices from the Repeatable Battery for the Assessment of Neuropsychological Status. Results generally replicated previous relationships between key medial temporal lobe regions and memory test performance, though comparison of NQ regions revealed statistically different values that were biased toward one version or the other depending on the region. NQ2 hippocampal z-scores explained additional variance in memory performance relative to % ICV values. Findings indicate that NQ1/2 medial temporal lobe volumes, especially age- and sex-based z-scores, hold clinical value, though caution is warranted when directly comparing volumes across NQ versions.

The superior fault tolerance of artificial neural network training with a fault/noise injection-based genetic algorithm

Artificial neural networks (ANNs) are powerful computational tools that are designed to replicate the human brain and adopted to solve a variety of problems in many different fields. Fault tolerance (FT), an important property of ANNs, ensures their reliability when significant portions of a network are lost. In this paper, a fault/noise injection-based (FIB) genetic algorithm (GA) is proposed to construct fault-tolerant ANNs. The FT performance of an FIB-GA was compared with that of a common genetic algorithm, the back-propagation algorithm, and the modification of weights algorithm. The FIB-GA showed a slower fitting speed when solving the exclusive OR (XOR) problem and the overlapping classification problem, but it significantly reduced the errors in cases of single or multiple faults in ANN weights or nodes. Further analysis revealed that the fit weights showed no correlation with the fitting errors in the ANNs constructed with the FIB-GA, suggesting a relatively even distribution of the various fitting parameters. In contrast, the output weights in the training of ANNs implemented with the use the other three algorithms demonstrated a positive correlation with the errors. Our findings therefore indicate that a combination of the fault/noise injection-based method and a GA is capable of introducing FT to ANNs and imply that the distributed ANNs demonstrate superior FT performance.

Detection of Alzheimer's disease signature in MR images seven years before conversion to dementia: Toward an early individual prognosis

Finding very early biomarkers of Alzheimer's Disease (AD) to aid in individual prognosis is of major interest to accelerate the development of new therapies. Among the potential biomarkers, neurodegeneration measurements from MRI are considered as good candidates but have so far not been effective at the early stages of the pathology. Our objective is to investigate the efficiency of a new MR-based hippocampal grading score to detect incident dementia in cognitively intact patients. This new score is based on a pattern recognition strategy, providing a grading measure that reflects the similarity of the anatomical patterns of the subject under study with dataset composed of healthy subjects and patients with AD. Hippocampal grading was evaluated on subjects from the Three-City cohort, with a followup period of 12 years. Experiments demonstrate that hippocampal grading yields prediction accuracy up to 72.5% (P < 0.0001) 7 years before conversion to AD, better than both hippocampal volume (58.1%, P = 0.04) and MMSE score (56.9%, P = 0.08). The area under the ROC curve (AUC) supports the efficiency of imaging biomarkers with a gain of 8.4 percentage points for hippocampal grade (73.0%) over hippocampal volume (64.6%). Adaptation of the proposed framework to clinical score estimation is also presented. Compared with previous studies investigating new biomarkers for AD prediction over much shorter periods, the very long followup of the Three-City cohort demonstrates the important clinical potential of the proposed imaging biomarker. The high accuracy obtained with this new imaging biomarker paves the way for computer-based prognostic aides to help the clinician identify cognitively intact subjects that are at high risk to develop AD.

A novel cortical thickness estimation method based on volumetric Laplace-Beltrami operator and heat kernel

Cortical thickness estimation in magnetic resonance imaging (MRI) is an important technique for research on brain development and neurodegenerative diseases. This paper presents a heat kernel based cortical thickness estimation algorithm, which is driven by the graph spectrum and the heat kernel theory, to capture the gray matter geometry information from the in vivo brain magnetic resonance (MR) images. First, we construct a tetrahedral mesh that matches the MR images and reflects the inherent geometric characteristics. Second, the harmonic field is computed by the volumetric Laplace-Beltrami operator and the direction of the steamline is obtained by tracing the maximum heat transfer probability based on the heat kernel diffusion. Thereby we can calculate the cortical thickness information between the point on the pial and white matter surfaces. The new method relies on intrinsic brain geometry structure and the computation is robust and accurate. To validate our algorithm, we apply it to study the thickness differences associated with Alzheimer's disease (AD) and mild cognitive impairment (MCI) on the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset. Our preliminary experimental results on 151 subjects (51 AD, 45 MCI, 55 controls) show that the new algorithm may successfully detect statistically significant difference among patients of AD, MCI and healthy control subjects. Our computational framework is efficient and very general. It has the potential to be used for thickness estimation on any biological structures with clearly defined inner and outer surfaces.

Obesity: Cognitive impairment and the failure to 'eat right'

A recent study has found that obese women (but not men) have difficulty inhibiting food-rewarded, but not money-rewarded, appetitive behaviour, suggesting that obesity is associated with cognitive deficits that could selectively promote food intake, perhaps in a sex-dependent manner.

Diffusion weighted imaging-based maximum density path analysis and classification of Alzheimer's disease

Characterizing brain changes in Alzheimer's disease (AD) is important for patient prognosis and for assessing brain deterioration in clinical trials. In this diffusion weighted imaging study, we used a new fiber-tract modeling method to investigate white matter integrity in 50 elderly controls (CTL), 113 people with mild cognitive impairment, and 37 AD patients. After clustering tractography using a region-of-interest atlas, we used a shortest path graph search through each bundle's fiber density map to derive maximum density paths (MDPs), which we registered across subjects. We calculated the fractional anisotropy (FA) and mean diffusivity (MD) along all MDPs and found significant MD and FA differences between AD patients and CTL subjects, as well as MD differences between CTL and late mild cognitive impairment subjects. MD and FA were also associated with widely used clinical scores. As an MDP is a compact low-dimensional representation of white matter organization, we tested the utility of diffusion tensor imaging measures along these MDPs as features for support vector machine based classification of AD.

Studying ventricular abnormalities in mild cognitive impairment with hyperbolic Ricci flow and tensor-based morphometry

Mild Cognitive Impairment (MCI) is a transitional stage between normal aging and dementia and people with MCI are at high risk of progression to dementia. MCI is attracting increasing attention, as it offers an opportunity to target the disease process during an early symptomatic stage. Structural magnetic resonance imaging (MRI) measures have been the mainstay of Alzheimer's disease (AD) imaging research, however, ventricular morphometry analysis remains challenging because of its complicated topological structure. Here we describe a novel ventricular morphometry system based on the hyperbolic Ricci flow method and tensor-based morphometry (TBM) statistics. Unlike prior ventricular surface parameterization methods, hyperbolic conformal parameterization is angle-preserving and does not have any singularities. Our system generates a one-to-one diffeomorphic mapping between ventricular surfaces with consistent boundary matching conditions. The TBM statistics encode a great deal of surface deformation information that could be inaccessible or overlooked by other methods. We applied our system to the baseline MRI scans of a set of MCI subjects from the Alzheimer's Disease Neuroimaging Initiative (ADNI: 71 MCI converters vs. 62 MCI stable). Although the combined ventricular area and volume features did not differ between the two groups, our fine-grained surface analysis revealed significant differences in the ventricular regions close to the temporal lobe and posterior cingulate, structures that are affected early in AD. Significant correlations were also detected between ventricular morphometry, neuropsychological measures, and a previously described imaging index based on fluorodeoxyglucose positron emission tomography (FDG-PET) scans. This novel ventricular morphometry method may offer a new and more sensitive approach to study preclinical and early symptomatic stage AD.

Magnetization-prepared rapid acquisition with gradient echo magnetic resonance imaging signal and texture features for the prediction of mild cognitive impairment to Alzheimer's disease progression

Early diagnoses of Alzheimer's disease (AD) would confer many benefits. Several biomarkers have been proposed to achieve such a task, where features extracted from magnetic resonance imaging (MRI) have played an important role. However, studies have focused exclusively on morphological characteristics. This study aims to determine whether features relating to the signal and texture of the image could predict mild cognitive impairment (MCI) to AD progression. Clinical, biological, and positron emission tomography information and MRI images of 62 subjects from the AD neuroimaging initiative were used in this study, extracting 4150 features from each MRI. Within this multimodal database, a feature selection algorithm was used to obtain an accurate and small logistic regression model, generated by a methodology that yielded a mean blind test accuracy of 0.79. This model included six features, five of them obtained from the MRI images, and one obtained from genotyping. A risk analysis divided the subjects into low-risk and high-risk groups according to a prognostic index. The groups were statistically different ([Formula: see text]). These results demonstrated that MRI features related to both signal and texture add MCI to AD predictive power, and supported the ongoing notion that multimodal biomarkers outperform single-modality ones.